SS-LSTM: A Hierarchical LSTM Model for Pedestrian Trajectory Prediction

By Hao Xue, Du Q. Huynh, Mark Reynolds. IEEE Workshop/Winter Conference on Applications of Computer Vision, 2018

411 citations recorded by the source corpus.

Pedestrian trajectory prediction is an extremely challenging problem because of the crowdedness and clutter of the scenes. Previous deep learning LSTM-based approaches focus on the neighbourhood influence of pedestrians but ignore the scene layouts in pedestrian trajectory prediction. In this paper, a novel hierarchical LSTM-based network is proposed to consider both the influence of social neighbourhood and scene layouts. Our SS-LSTM, which stands for Social-Scene-LSTM, uses three different LSTMs to capture person, social and scene scale information. We also use a circular shape neighbourhood setting instead of the traditional rectangular shape neighbourhood in the social scale. We evaluate our proposed method against two baseline methods and a state-of-art technique on three public datasets. The results show that our method outperforms other methods and that using circular shape neighbourhood improves the prediction accuracy.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/498c8abcef3b709be97167ea5870e85d0c586945

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Citations in the corpus

Human motion trajectory prediction: a survey (2019, methodology): “…and Srinivasaraghavan 2017; Vemula et al. 2018; Radwan et al. 2018a; Pfeiffer et al. 2018; Shi et al. 2019; Zhao et al. 2019; Hasan et al. 2018; Xue et al. 2018; Su et al. 2017; Sadeghian et al. 2018b), implicitly as a result of pixel-wise prediction (Walker et al. 2014), or by learning a…”
SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraints (2018, background): “Few recent approaches [14, 26, 4], to some extent, incorporate both the scene and social factors into their models.”
PIE: A Large-Scale Dataset and Models for Pedestrian Intention Estimation and Trajectory Prediction (2019, background): “Trajectory-based algorithms rely on observation of pedestrians’ past motion history and predict the location of the pedestrians in the future using contextual information such as 3D depth [14, 34, 39], social interactions [25, 42, 1, 31, 41], the ego-vehicle dynamics and the scene structure [16, 17,”
A survey on long short-term memory networks for time series prediction (2021, background): “Thus, hierarchical LSTM networks can be viewed as attention-based networks according to the description in [13].”